Skip to main content

Advertisement

Springer Nature Link
Log in

Dual attention granularity network for vehicle re-identification

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Vehicle re-identification (Re-ID) aims to search for a vehicle of interest in a large video corpus captured by different surveillance cameras. The identification process considers both coarse-grained similarity (e.g., vehicle Model/color) and fine-grained similarity (e.g., windshield stickers/decorations) among vehicles. Coarse-grained and fine-grained similarity comparisons usually attend to very different visual regions, which implies that two different attention modules are required to handle different granularity comparisons. In this paper, we propose a dual attention granularity network (DAG-Net) for Vehicle Re-ID. The DAG-Net consists of three main components: (1) A convolutional neural network with a dual-branch structure is proposed as the backbone feature extractor for coarse-grained recognition (i.e., vehicle Model) and fine-grained recognition (i.e., vehicle ID); (2) the self-attention model is added to each branch, which enables the DAG-Net to detect different regions of interest (ROIs) at both coarse-level and fine-level with the assistance of the part-positioning block; (3) finally, we obtain refined regional features of the ROIs from the sub-networks ROIs. As a result, the proposed DAG-Net is able to selectively attend to the most discriminative regions for coarse/fine-grained recognition. We evaluate our method on two Vehicle Re-ID datasets: VeRi-776 and VehicleID. Experiments show that the proposed method can bring substantial performance improvement and achieve state-of-the-art accuracy. In addition, we focus on the different effects of regional features and global features. We conduct experiments to verify it in the PKU dataset and discuss the effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Radenović F, Iscen A, Tolias G, Avrithis Y, Chum O (2018) Revisiting oxford and paris: Large-scale image retrieval benchmarking In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 5706–5715

  2. Vo N, Jiang L, Sun C, Murphy K, Li LJ, Fei-Fei L, Hays J (2019) Composing text and image for image retrieval-an empirical odyssey. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 6439–6448

  3. Ng T, Balntas V, Tian Y, Mikolajczyk K (2020) Solar: second-order loss and attention for image retrieval. In: European conference on computer vision. Springer, pp 253–270

  4. Yan C, Gong B, Wei Y, Gao Y (2020) Deep multi-view enhancement hashing for image retrieval. IEEE Trans Pattern Anal Mach Intell 43(4):1445–1451

    Article  Google Scholar 

  5. Ji Y, Zhang H, Jie Z, Ma L, Wu Q (2020) CASNet: a cross-attention siamese network for video salient object detection. IEEE Trans Neural Netw Learn Syst 32(6):2676–2690

    Article  Google Scholar 

  6. Liao S, Hu Y, Zhu X, Li SZ (2015) Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2197–2206)

  7. Ding S, Lin L, Wang G, Chao H (2015) Deep feature learning with relative distance comparison for person re-identification. Pattern Recognit 48(10):2993–3003

    Article  Google Scholar 

  8. Li W, Zhu X, Gong S (2018) Harmonious attention network for person re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2285–2294

  9. Liu X, Liu W, Mei T, Ma H (2017) Provid: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimed 20(3):645–658

    Article  Google Scholar 

  10. Liu X, Liu W, Mei T, Ma H (2016) A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: European conference on computer vision. Springer, pp 869–884

  11. Cai J, Deng J, Aftab MU, Khokhar MS, Kumar R et al (2019) Efficient and deep vehicle re-identification using multi-level feature extraction. Appl Sci 9(7):1291

    Article  Google Scholar 

  12. Liu X, Zhang S, Huang Q, Gao W (2018) Ram: a region-aware deep model for vehicle re-identification. In: 2018 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1–6

  13. He B, Li J, Zhao Y, Tian Y (2019) Part-regularized near-duplicate vehicle re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3997–4005

  14. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779–788

  15. Zhang X, Zhao Z, Zhang H, Wang S, Li Z (2018) Unsupervised geographically discriminative feature learning for landmark tagging. Knowl-Based Syst 149:143–154

    Article  Google Scholar 

  16. Mirjalili V, Raschka S, Ross A (2020) PrivacyNet: semi-adversarial networks for multi-attribute face privacy. IEEE Trans Image Process 29:9400–9412

    Article  Google Scholar 

  17. Raziyeh Z, Mohammad K (2019) On the construction of multi-relational classifier based on canonical correlation analysis. IEEE Trans Image Process 17(2):23–43

    Google Scholar 

  18. Jiang N, Xu Y, Zhou Z, Wu W (2018) Multi-attribute driven vehicle re-identification with spatial-temporal re-ranking. In: 2018 25th IEEE international conference on image processing (ICIP). pp 858–862

  19. Liu H, Tian Y, Yang Y, Pang L, Huang T (2016) Deep relative distance learning: tell the difference between similar vehicles. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2167–2175

  20. Qian J, Jiang W, Luo H, Yu H (2020) Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification. Meas Sci Technol 31(9):095401

    Article  Google Scholar 

  21. Zhou M, Niu Z, Wang L, Gao Z, Zhang Q, Hua G (2020) Ladder loss for coherent visual-semantic embedding In: Proceedings of the AAAI conference on artificial intelligence, vol 34. pp 13050–13057

  22. Yan K, Tian Y, Wang Y, Zeng W, Huang T (2017) Exploiting multi-grain ranking constraints for precisely searching visually-similar vehicles. In: Proceedings of the IEEE international conference on computer vision. pp 562–570

  23. Wei XS, Zhang CL, Liu L, Shen C, Wu J (2018) Coarse-to-fine: a rnn-based hierarchical attention model for vehicle re-identification. In: Asian conference on computer vision. Springer, pp 575–591

  24. Tumrani S, Deng Z, Lin H, Shao J (2020) Partial attention and multi-attribute learning for vehicle re-identification. Pattern Recognit Lett 138:290–297

    Article  Google Scholar 

  25. Lou Y, Bai Y, Liu J, Wang S, Duan L (2019) Veri-wild: a large dataset and a new method for vehicle re-identification in the wild. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 3235–3243

  26. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 815–823

  27. Guo H, Zhu K, Tang M, Wang J (2019) Two-level attention network with multi-grain ranking loss for vehicle re-identification. IEEE Trans Image Process 28(9):4328–4338

    Article  MathSciNet  MATH  Google Scholar 

  28. Zhao Y, Shen C, Wang H, Chen S (2019) Structural analysis of attributes for vehicle re-identification and retrieval. IEEE Trans Intell Trans Syst 21(2):723–734

    Article  Google Scholar 

  29. Zhou Y, Shao L (2018) Aware attentive multi-view inference for vehicle re-identification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 6489–6498

  30. Teng S, Liu X, Zhang S, Huang Q (2018) Scan: spatial and channel attention network for vehicle re-identification. In: Pacific Rim conference on multimedia. Springer, pp 350–361

  31. Wang H, Peng J, Chen D, Jiang G, Zhao T, Fu X (2020) Attribute-guided feature learning network for vehicle reidentification. IEEE Multimed 27(4):112–121

    Article  Google Scholar 

  32. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2818–2826

  33. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008

  34. Wang X, Girshick R, Gupta A, He V (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7794–7803

  35. Yang L, Luo P, Loy CC, Tang X (2015) A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 3973–3981

  36. Liu X, Liu W, Ma H, Fu H (2016) Large-scale vehicle re-identification in urban surveillance videos. In: 2016 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1–6

  37. Kanacı A, Zhu X, Gong S (2018) Vehicle re-identification in context. In: German conference on pattern recognition. Springer, pp 377–390

  38. Wang Z, Tang L, Liu X, Yao Z, Yi S, Shao J, Yan J, Wang S, Li H, Wang X (2017) Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 379–387

  39. Fernandez M, Moral P, Garcia-Martin A, Martinez J (2021) vehicle re-identification based on ensembling deep learning features including a synthetic training dataset. In: Proceedings of the IEEE international conference on computer vision. 4068–4076

  40. Meng D, Li L, Liu X, Li Y, Yang S, Zha Z, Gao X, Wang S, Huang Q (2020) Parsing-based view-aware embedding network for vehicle re-identification. In: Proceedings of the IEEE international conference on computer vision. pp. 7103–7112

  41. Chen T, Liu C, Wu C, Chien S (2020) Orientation-aware vehicle re-identification with semantics-guided part attention network. In: European conference on computer vision. Springer, pp 330–346

Download references

Funding

This work was partially supported by National Key R&D Program of China (2018YFB1305200). This publication was partially funded by the National Natural Science Foundation of China (62020106004, 61876167) and the Natural Science Foundation of Zhejiang Province (LY20F030017).

Author information

Authors and Affiliations

  1. Tianjin University Of Technology, Tianjin, 300384, China

    Jianhua Zhang & Shengyong Chen

  2. Zhejiang University of Technology, Hangzhou, 310012, China

    Jingbo Chen & Linjie Bian

  3. University of Queensland, Brisbane, QLD, 4072, Australia

    Jiewei Cao

  4. Hangzhou Normal University, Hangzhou, 311121, China

    Ruyu Liu

Authors
  1. Jianhua Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jingbo Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jiewei Cao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ruyu Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Linjie Bian
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Shengyong Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ruyu Liu.

Ethics declarations

Conflicts of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Chen, J., Cao, J. et al. Dual attention granularity network for vehicle re-identification. Neural Comput & Applic 34, 2953–2964 (2022). https://doi.org/10.1007/s00521-021-06559-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06559-6

Keywords

Profiles

  1. Ruyu Liu View author profile